Advanced meta-analysis & evidence synthesis

Rigorous evidence synthesis, statistical analysis and systematic-review support for researchers across medicine, life sciences, management, economics, psychology, education and the social sciences.

Illustrative forest plot of seven simulated studiesEach study is shown as a square with a horizontal line for its 95% confidence interval. The pooled random-effects estimate is shown as a diamond, with a standardized mean difference of 0.31 (95% CI 0.11 to 0.51). The data are simulated.StudySMD [95% CI]Study A0.62 [0.27, 0.97]Study B0.08 [-0.16, 0.32]Study C0.78 [0.29, 1.27]Study D0.30 [0.10, 0.50]Study E-0.18 [-0.57, 0.21]Study F0.52 [0.23, 0.81]Study G0.20 [-0.07, 0.47]Random effects0.31 [0.11, 0.51]-0.50.00.51.0Favors controlFavors interventionHeterogeneity: I² = 68%, τ² = 0.046
Illustrative forest plot. The studies and values are simulated for display and do not represent real research. The diamond is the pooled random-effects estimate.

Who we are

My Meta-Analysis is a research-support organization specializing in meta-analysis, systematic reviews and statistical analysis.

What we do

We support the full evidence-synthesis workflow: protocol, search, screening, extraction, analysis, reporting and journal submission.

Who we serve

Researchers and research teams preparing reviews and analyses for publication, from clinical medicine to management and economics.

Core services

Each service follows the relevant published methodological standards and is scoped to the study design, not to a template.

Meta-analysis

Pooling of effect estimates across studies, with assessment of heterogeneity, sensitivity and publication bias.

Random-effects models, forest and funnel plots, subgroup analysis

Systematic review

A documented search, screening, appraisal and synthesis, reported against PRISMA 2020.

Protocol, search strategy, risk of bias, flow diagram

Network meta-analysis

Comparison of several interventions at once, using direct and indirect evidence.

Transitivity, consistency, treatment rankings, league tables

Bayesian meta-analysis

Synthesis with explicit prior assumptions, useful when few studies are available or prior evidence should be formally incorporated.

Prior specification, posterior intervals, sensitivity to priors

Meta-regression

Examination of whether study-level characteristics explain differences between results.

Moderators, mixed-effects models, limits of study-level inference

Statistical analysis

Design-appropriate analysis of primary research data, with reproducible code and a clear methods description.

Model selection, survival analysis, sample-size considerations

Publication support

Reporting-guideline checks, manuscript editing, journal selection and responses to reviewer comments.

PRISMA and MOOSE adherence, submission materials, revisions

Methods we work with

From pairwise meta-analysis to network and Bayesian models, and from systematic reviews to scoping and qualitative synthesis.

Quantitative synthesis

Random-effects meta-analysis
Allows true effects to vary between studies and reports their average and spread.
Network meta-analysis
Compares multiple interventions through a connected network of trials.
Bayesian meta-analysis
Combines study data with prior distributions to produce posterior estimates.
Meta-regression
Relates effect sizes to study-level moderators.
Diagnostic accuracy meta-analysis
Pools sensitivity and specificity across test-accuracy studies.
Individual participant data meta-analysis
Synthesizes participant-level data rather than published summaries.

Review designs

Systematic review
Structured identification, appraisal and synthesis of all relevant studies.
Scoping review
Maps the extent and nature of evidence without pooling effects.
Umbrella review
Synthesizes evidence from existing systematic reviews.
Rapid review
Streamlines review methods to meet a time constraint, with stated trade-offs.
Qualitative evidence synthesis
Integrates findings from qualitative studies, including meta-synthesis.
Living systematic review
Updated continually as new evidence appears.

Not every method suits every field or dataset. Choosing the right design, or recommending against pooling, is part of the work.

Research areas

Medicine and health sciences are a flagship area. The same methods apply, with discipline-specific study designs and outcomes, across the fields below.

Medical & health sciences

Clinical research, public health, epidemiology, nursing, pharmacy, oncology, cardiology, neurology, psychiatry

Life & biological sciences

Genetics, genomics, microbiology, immunology, neuroscience, ecology, evolutionary biology

Psychology & behavioral sciences

Clinical, cognitive, social, developmental and educational psychology; mental health and addiction research

Management & business

Organizational behavior, human resource management, marketing, entrepreneurship, supply chain

Economics & econometrics

Health, development, labor, environmental and behavioral economics; economic policy

Education

Higher education, STEM education, medical education, online learning, special education

Social sciences

Sociology, social work, political science, public policy, criminology, migration studies

Environment & sustainability

Climate change, conservation, biodiversity, water resources, environmental health

Agriculture & food sciences

Agronomy, crop and soil science, animal and veterinary research, food science

Sports & exercise sciences

Exercise physiology, sports medicine, physical activity, injury prevention, rehabilitation

Engineering & technology

Biomedical, environmental and energy engineering; computer science and machine learning

Why My Meta-Analysis

A synthesis is only as useful as the method behind it. These are the commitments the work is organized around.

Methods matched to the question

The review design and statistical model follow from the research question and the available studies, not from a fixed package.

Published standards

Reviews are planned and reported against recognized guidance, including the Cochrane Handbook, PRISMA 2020 and the relevant extensions.

Clear boundaries

We provide research and evidence-synthesis support. We do not provide clinical advice, and results are not patient-specific guidance.

Transparent authorship and tools

Our research integrity and authorship statement and AI-use policy set out how work is done and credited.

How the process works

Most projects move through five stages. Some, such as a statistical re-analysis for a revision, need only one or two.

  1. Scope

    We review your question, study type, target journal and deadline, and agree what is feasible.

  2. Protocol

    Eligibility criteria, outcomes and analysis plan are specified, and registration is prepared where appropriate.

  3. Search and selection

    Searches are designed and documented, studies screened, and data extracted using a defined form.

  4. Analysis and appraisal

    Risk of bias and certainty of evidence are assessed, and the pre-specified analyses are run.

  5. Reporting

    Results are written up against the relevant reporting guideline, with support through submission and revision.

Research deliverables

What you receive depends on the service. A full review project can include:

  • Protocol and registration-ready summary
  • Search strategies for each database searched
  • Screening and PRISMA flow records
  • Extraction dataset and risk-of-bias assessments
  • Analysis report with forest and funnel plots
  • Methods and results text prepared for the manuscript
  • Analysis code and outputs so results can be reproduced

Quality and reproducibility

Evidence synthesis should be checkable by someone else. The working practices below are designed to make that possible.

  • Pre-specified analysis plan fixed before results are seen
  • Independent screening and extraction where the protocol calls for it
  • Documented software and versions for every analysis
  • Audit trail of decisions, exclusions and deviations from protocol
  • Confidential handling of unpublished manuscripts and data

Guides for researchers

Short, referenced explanations of the concepts that come up most often when planning and reporting an evidence synthesis.

What is meta-analysis?

A statistical method for combining quantitative results from separate studies that address the same question, to estimate an overall effect and examine why results differ.

Fixed-effect or random-effects model?

A fixed-effect model assumes one true effect shared by every study. A random-effects model assumes true effects vary, and estimates their average and spread.

What is PRISMA 2020?

A reporting guideline for systematic reviews, consisting of a 27-item checklist and a flow diagram that documents how studies were identified and selected.

Frequently asked questions

Answers to common questions about reviews and meta-analyses. More are in the full FAQ.

What is the difference between a systematic review and a meta-analysis?

A systematic review is a structured method for identifying, appraising and summarizing the relevant studies on a question. A meta-analysis is a statistical technique for combining the numerical results of those studies. Many systematic reviews include a meta-analysis, but a review can be reported narratively when the studies are too different to pool.

Does a meta-analysis need to be part of a systematic review?

In most cases it should be. A meta-analysis is normally built on a systematic, documented search and selection of studies. Pooling studies chosen informally risks biased estimates, because the result then depends on which studies happened to be found.

How many studies are needed for a meta-analysis?

There is no universal minimum. Two studies can be combined mathematically, but with few studies the estimate of between-study variation is imprecise, and methods such as meta-regression or tests for publication bias become unreliable. Whether pooling is meaningful depends on how similar the studies' questions are as much as on how many there are.

Which reporting guideline applies to a systematic review?

PRISMA 2020 is the standard reporting guideline for systematic reviews, with extensions for scoping reviews, diagnostic accuracy reviews, network meta-analyses and other designs. Some journals also ask for MOOSE when a meta-analysis is based on observational studies. The target journal's instructions for authors decide which applies.

Is meta-analysis suitable for every research field?

No. It requires studies that measure comparable outcomes in a comparable way. It is well established in clinical medicine, psychology and education, and used in parts of the social, management and ecological sciences, but it is less common elsewhere. In some fields a systematic or scoping review without pooling is the more appropriate design.

Do you provide clinical advice?

No. My Meta-Analysis provides research and evidence-synthesis support. The results of a review or analysis are not patient-specific medical advice.

Tell us about your research

Describe your question, study type and target journal. We will respond with the approach we would recommend and what we would need to begin.